We have evaluated our method on several public benchmarks. On ordinary speed sequences, our method achieves comparable accuracy of camera tracking against the state-of-the-art methods, without needing post-processing such as loop closure detection or global pose optimization. On the ETH3D benchmark [1]}, our method successfully reconstructs those challenging sequences with fast camera motion (i.e., shaking cameras) on which all previous methods failed. We also contribute a new benchmark named FastCaMo which encompasses both synthetic and real captured sequences with fast camera motion (\(3\sim 4\)  times faster than the existing datasets).
Extensive quantitative and qualitative evaluations demonstrate the significant advantage of our method on fast-motion camera tracking and dense reconstruction.
